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Record W2383850152

A Real-time ECG Analysis Algorithm for Mobile ECG Tele-monitoring System

2014· article· en· W2383850152 on OpenAlex
Guo Wen-lin

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJisuanji fangzhen · 2014
Typearticle
Languageen
FieldMedicine
TopicECG Monitoring and Analysis
Canadian institutionsDalhousie University
Fundersnot available
KeywordsQRS complexAlgorithmVentricular tachycardiaNormal Sinus RhythmComputer scienceVentricular fibrillationProcess (computing)RhythmReal-time computingPattern recognition (psychology)Internal medicineArtificial intelligenceMedicineAtrial fibrillation
DOInot available

Abstract

fetched live from OpenAlex

In the mobile electrocardiogram( ECG) tele- monitoring system,the real- time QRS wave detection and analysis of arrhythmia are crucial issues. This paper firstly utilizes Pan- Tompkins algorithm to detect QRS wave,and distinguishes sinus rhythm( SR) and arrhythmia; then uses Lempel- Ziv( LZ) complexity algorithm based on coarse- graining process to analyze the arrhythmia. By testing on 100 sinus rhythm segments,120 ventricular tachycardia( VT) segments and 60 ventricular fibrillation( VF) segments from MIT /BIH database,the result shows that LZ complexity algorithm based on K- Mean coarse- graining process can separate VT and VF effectively,which is a practical method for arrhythmia analysis.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.926
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.011
GPT teacher head0.296
Teacher spread0.285 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it